Open Access

ARTICLE

Nighttime Intelligent UAV-Based Vehicle Detection and Classification Using YOLOv10 and Swin Transformer

Abdulwahab Alazeb¹, Muhammad Hanzla², Naif Al Mudawi^1,*, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

1 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
2 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, 52571, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, 02841, Republic of Korea

* Corresponding Author: Naif Al Mudawi. Email:

Computers, Materials & Continua 2025, 84(3), 4677-4697. https://doi.org/10.32604/cmc.2025.065899

Received 24 March 2025; Accepted 28 May 2025; Issue published 30 July 2025

Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms for robust performance. Extensive experimentation validates our approach, achieving detection mAP@0.5 scores of 91.5% (UAVDT) and 89.7% (VisDrone), alongside classification accuracies of 95.50% and 92.67%, respectively. These results outperform state-of-the-art methods by up to 5.10% in accuracy and 4.2% in mAP, demonstrating the framework’s effectiveness for real-time aerial surveillance and intelligent traffic management in challenging nighttime environments.

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Keywords

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